Soft Sensor Development for Real-Time Interface Tracking in Multiple Product Pipelines Based on Knowledge and Data

Abstract

Summary Petroleum products are usually consecutively transported in the same multiple-product pipeline, and the occurrence of mixed oil happens during the process. Accurately tracking the mixed oil interface is essential for the optimal scheduling of oil transportation. However, complicated operating conditions and unavoidable measurement noise pose challenges to methods for locating the interface. The data-driven modeling method is a potential solution, but it may face limitations due to issues of overfitting, especially when the data set is contaminated with noise. To tackle such problems, in this paper, we propose a knowledge-informed Bayesian-Gaussian mixture regression (KIBGMR) model to enable the real-time tracking of the interface. The KIBGMR employs finite Gaussian distribution to learn the multimode characteristics of input data, including the hydrothermal data, measured density of tail oil, and the velocity of interface transportation and output data including the measured density of tail oil and interface transportation velocity. Subsequently, it utilizes the prior knowledge related to the regression coefficient through the Bayesian treatment. Evaluations demonstrate that the R2 index achieved by the proposed model in predicting the interface arrival time is greater than 0.98, even with the contaminated data set. This research can help operators accurately grasp the location of the mixed oil interface, formulate reasonable valve switch operations for better management of the mixed oil section, and provide a reference for the method of knowledge-data hybrid modeling.